Establishing Monoclonal Gammopathy of Undetermined Significance as an Independent Pre-Disease State of Multiple Myeloma Using Raman Spectroscopy, Dynamical Network Biomarker Theory, and Energy Landscape Analysis.

Multiple myeloma (MM) is a cancer of plasma cells. Normal (NL) cells are considered to pass through a precancerous state, such as monoclonal gammopathy of undetermined significance (MGUS), before transitioning to MM. In the present study, we acquired Raman spectra at three stages-834 NL, 711 MGUS, and 970 MM spectra-and applied the dynamical network biomarker (DNB) theory to these spectra. The DNB analysis identified MGUS as the unstable pre-disease state of MM and extracted Raman shifts at 1149 and 1527-1530 cm-1 as DNB variables. The distribution of DNB scores for each patient showed a significant difference between the mean values for MGUS and MM patients. Furthermore, an energy landscape (EL) analysis showed that the NL and MM stages were likely to become stable states. Raman spectroscopy, the DNB theory, and, complementarily, the EL analysis will be applicable to the identification of the pre-disease state in clinical samples.

Practices, Potential, and Perspectives for Detecting Predisease Using Raman Spectroscopy.

Raman spectroscopy shows great potential for practical clinical applications. By analyzing the structure and composition of molecules through real-time, non-destructive measurements of the scattered light from living cells and tissues, it offers valuable insights. The Raman spectral data directly link to the molecular composition of the cells and tissues and provides a "molecular fingerprint" for various disease states. This review focuses on the practical and clinical applications of Raman spectroscopy, especially in the early detection of human diseases. Identifying predisease, which marks the transition from a healthy to a disease state, is crucial for effective interventions to prevent disease onset. Raman spectroscopy can reveal biological processes occurring during the transition states and may eventually detect the molecular dynamics in predisease conditions.

Application of the Dynamical Network Biomarker Theory to Raman Spectra.

The dynamical network biomarker (DNB) theory detects the early warning signals of state transitions utilizing fluctuations in and correlations between variables in complex systems. Although the DNB theory has been applied to gene expression in several diseases, destructive testing by microarrays is a critical issue. Therefore, other biological information obtained by non-destructive testing is desirable; one such piece of information is Raman spectra measured by Raman spectroscopy. Raman spectroscopy is a powerful tool in life sciences and many other fields that enable the label-free non-invasive imaging of live cells and tissues along with detailed molecular fingerprints. Naïve and activated T cells have recently been successfully distinguished from each other using Raman spectroscopy without labeling. In the present study, we applied the DNB theory to Raman spectra of T cell activation as a model case. The dataset consisted of Raman spectra of the T cell activation process observed at 0 (naïve T cells), 2, 6, 12, 24 and 48 h (fully activated T cells). In the DNB analysis, the F-test and hierarchical clustering were used to detect the transition state and identify DNB Raman shifts. We successfully detected the transition state at 6 h and related DNB Raman shifts during the T cell activation process. The present results suggest novel applications of the DNB theory to Raman spectra ranging from fundamental research on cellular mechanisms to clinical examinations.

Early detection of virus infection in live human cells using Raman spectroscopy.

Virus infection of a human cell was determined only 3 h after invagination. We used viral vector Ad-CMV-control (AdC), which lacks the E1 gene coding for early polypeptide 1 (E1). AdC can replicate in human embryonic kidney 293 (HEK293) cells into which the E1 gene has been transfected. According to partial least-square regression discriminant analysis, it was assumed that two kinds of reaction take place in the cell during viral invasion. The first response of the cell was determined 3 h after the virus invasion, and the second one was determined ∼9 h later. The first one seems to be due to compositional changes in DNA. Analysis of large-scale datasets strongly indicated that the second reaction can be attributed to a reduction in protein concentration or uptake of phenylalanine into the nucleus.

Raman endoscopy for monitoring the anticancer drug treatment of colorectal tumors in live mice.

A miniaturized Raman endoscope (mRE) system was employed to study the effects of anticancer treatment on colorectal tumors in a live murine model. The endoscope is narrow enough to observe the inside of the mouse colon under anesthesia. It has a channel for a ball lens mounted on a hollow fiber Raman probe (BHRP) to measure any targeted point under the visual control of the endoscope. Colorectal cancer tissue was observed to study the alterations of the tissue in response to anticancer drug treatment. Three anticancer drugs, 5-fluorouracil (5-FU), cisplatin (CDDP), and docetaxel, were employed. Although no alteration was recognized in the endoscopic visual observations at 2 weeks after the drug treatment, the Raman spectra obtained in the live mouse colon indicated that molecular changes of lipids and proteins were observed. This study demonstrates that in situ Raman analysis is highly sensitive for detecting the effects of anticancer drugs.

Diagnosis of early-stage esophageal cancer by Raman spectroscopy and chemometric techniques.

Esophageal cancer is a disease with high mortality. In order to improve the 5 year survival rate after cancer treatment, it is important to develop a method for early detection of the cancer and for therapy support. There is increasing evidence that Raman spectroscopy, in combination with chemometric analysis, is a powerful technique for discriminating pre-cancerous and cancerous biochemical changes. In the present study, we used Raman spectroscopy to examine early-stage (stages 0 and I) esophageal cancer samples ex vivo. Comparison between the Raman spectra of cancerous and normal samples using a t-test showed decreased concentrations of glycogen, collagen, and tryptophan in cancerous tissue. Partial least squares regression (PLSR) analysis and self-organization maps (SOMs) discriminated the datasets of cancerous and normal samples into two groups, but there was a relatively large overlap between them. Linear discriminant analysis (LDA) based on Raman bands found in the t-test was able to predict the tissue types with 81.0% sensitivity and 94.0% specificity.

Raman endoscopy for the in situ investigation of advancing colorectal tumors in live model mice.

The aim of the present study is to evaluate the capability of a miniaturized Raman endoscope (mRE) system to monitor the advancement of colorectal tumors in model mice as a method that is noninvasive to the tumor itself. Nevertheless, the endoscope is narrow enough to observe the inside of the mouse colon in such a way that is semi-noninvasive to the animal. However, the mRE system allowed the visualization and Raman spectral measurement of any targeted point within the colorectal tumor in model mice under anesthesia, without damaging the tissue (i.e., noninvasively). Continuous monitoring of the same tumor allowed the observation of alterations in its molecular composition and size, along with its advancement. The tumor lesion was discriminated from normal tissues of the control mouse with an accuracy of 86.8%. We succeeded in visualizing and performing Raman spectral observations on a shrinking polyp type tumor. The Raman analysis suggested that it was not cured but supposedly transformed to another tumor type.